Everyone’s waiting for Nvidia to confirm this week’s most interesting tech deal: A reported $13 billion acquisition of Hugging Face, a platform for sharing open weight AI models and benchmarks.
Now best known as the target for a team of reward-hacking OpenAI agents, Hugging Face is at the center of the ecosystem of developers building and deploying LLMs that aren’t owned by frontier labs. Think of it as a kind of GitHub for the AI era.
Rumors of that deal come after Nvidia struck a $6 billion agreement with Poolside, an open-weight model builder, that will see most of its employees move to the chip-making giant. And two weeks ago, Stripe acquired OpenRouter, the top provider of open-weight models to businesses, for more than $7 billion.
That’s a lot of capital pouring into a sector based on giving stuff away, and it reflects the latest trends in the AI sector.
For Nvidia, there’s a need to avoid further dependence on its deals with the major hyperscalers and frontier labs. That’s particularly the case when major AI model builders like OpenAI and Google are also building their own inference chips, like OpenAI’s Jalapeño, whose capabilities were announced this week. If model builders are making chips, Nvidia wants a chunk of the model-making business.
Nvidia already builds its own Nemotron family of open-weight models, but their uptake hasn’t been huge. By taking control of the largest US developer space for open models, the company will have access to a mass of users it can drive to its chips and standards.
There are also growing questions about the cost of AI inference, which has companies exploring cheaper models built by Chinese companies like Moonshot, DeepSeek and Alibaba. Right now, adoption is relatively small but growing—just 6% of companies use open-weight models, according to a survey of spending data by Ramp, or just 2% of software engineers surveyed by Jellyfish, which makes tools for developers.
Nik Albarran, the AI product lead at Jellyfish, told TechCrunch that open weight models are primarily used by companies whose products rely on repeated inference workloads, like those providing customer service chats. Because these are high-volume tasks with a lot of repetition, an open-weight model can be tuned to answer the questions cheaply.
That’s certainly how Stripe has framed its OpenRouter acquisition. “Tokens are the central currency for companies building with AI, and it’s clear that the real-world economic potential will depend on making good use of scarce compute resources,” Patrick Collison, Stripe’s cofounder and CEO, said in a statement.
For coding and agentic tasks, however, varying requests and more reasoning mean that frontier models often win out, in part because the proprietary labs provide easier access, and in some cases a token subsidy. Albarran says that as companies dial in AI workflows, it will be easier to turn to open models. Still, the main reason companies look to those models now is for control and configurability, not because of spending concerns.
“There are not many companies where that is the case yet…[but] if the prices continue to go up from the frontier labs, more and more companies will be forced to at least consider it,” Albarran told TechCrunch. “When your AI driven workflows are much more mature, that’s when it makes sense to invest in self-hosting models.”
Lin Qiao is the CEO of Fireworks, a leading open weight models router and host for corporate users that is often discussed as a potential acquisition for a tech giant. Qiao says her company processes 40 trillion tokens a day, more than either of Gemini or OpenAI’s APIs.
Fireworks’ bet is on model diversity: As LLMs proliferate and improve, it will be easier for companies to train them specifically for their needs. “Every single app company should consider hiring an in-house researcher,” she told TechCrunch last week. “They can use their product and product data to build their own model. The future is actually specialized intelligence. Literally, every single company should have their own model per use case, and that will happen automatically.”
It’s easy to forget how early we are in the development of AI as a tool and a business. The dominance of OpenAI and Anthropic, however, isn’t inevitable. As the tech giants look to hedge their bets on the biggest labs, the allure of open technology is proving tough to resist.
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